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Modeling and Prediction of Carbon Monoxide during the Start-Up in ICE through VARX Regression

Author

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  • Alejandro Garcia-Basurto

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

  • Angel Perez-Cruz

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

  • Aurelio Dominguez-Gonzalez

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

  • Juan J. Saucedo-Dorantes

    (Engineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Av. Río Moctezuma 249, San Juan del Rio 76807, Querétaro, Mexico)

Abstract

In a global society that is increasingly interrelated and focused on mobility, carbon monoxide emissions derived from internal combustion vehicles remain the most important factor that must be addressed to improve environmental quality. Certainly, air pollution generated by internal combustion engines threatens human health and the well-being of the planet. In this regard, this paper aims to address the urgent need to understand and face the CO emissions produced by internal combustion vehicles; therefore, this work proposes a mathematical model based on Auto-Regressive Exogenous that predicts the CO percentages produced by an internal combustion engine during its start-up. The main goal is to establish a strategy for diagnosing excessive CO emissions caused by changes in the engine temperature. The proposed CO emissions modeling is evaluated under a real dataset obtained from experiments, and the obtained results make the proposed method suitable for being implemented as a novel diagnosis tool in automotive maintenance programs.

Suggested Citation

  • Alejandro Garcia-Basurto & Angel Perez-Cruz & Aurelio Dominguez-Gonzalez & Juan J. Saucedo-Dorantes, 2024. "Modeling and Prediction of Carbon Monoxide during the Start-Up in ICE through VARX Regression," Energies, MDPI, vol. 17(11), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2493-:d:1399591
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    References listed on IDEAS

    as
    1. Xu, Bin & Lin, Boqiang, 2015. "Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach," Energy, Elsevier, vol. 83(C), pages 486-495.
    2. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    3. Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
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